Industrial internet of things with dual independent platform and control methods thereof

ABSTRACT

The present disclosure discloses an Industrial Internet of Things (IIoT) with a dual independent platform, which comprises a user platform, a service platform, a management platform, a sensor network platform and an object platform that interact in turn. The service platform adopts centralized layout, and the management platform and the sensor network platform adopt independent layout. The present disclosure also discloses a control method of the IIoT with the dual independent platform. The present disclosure builds the IIoT based on the five platform structure, in which the sensor network platform and the management platform are arranged independently, and each corresponding platform includes a plurality of independent sub-platforms, so that the independent sensor network platform and the management platform can be used for each production line device to form an independent data processing channel and transmission channel, and reduce the data processing capacity and transmission capacity of each platform.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority of Chinese Application No.202210340263.1, filed on Apr. 2, 2022, the entire contents of which arehereby incorporated by reference.

TECHNICAL FIELD

The present disclosure relates to intelligent manufacturing techniques,and specifically relates to IIoT with a dual independent platform andcontrol methods thereof.

BACKGROUND

In large factories or workshops, the same process, accessories orproducts may need to be manufactured with multiple production linedevices of the same type at the same time to complete the manufacturingrequirements in large quantities or within the specified processingtime.

In the prior art, when multiple production line devices of the same typeperform manufacturing, different production line device oftenmanufacture independently, and the processing parameters of eachproduction line equipment are different, resulting in differentproduction line equipment having high or low quality for finishedproducts. In order to improve the quality of finished products of allproduction line equipment of the same type, it is not only necessary tofind out the production line equipment with the best quality of finishedproducts, It is also necessary to extract the manufacturing parametersof the production line equipment, and finally update the manufacturingparameters of other production line equipment, which not only has a hugeworkload, time-consuming and labor-consuming, but also affects themanufacturing operations of all production line equipment. Moreover,even if the manufacturing parameters corresponding to the productionline equipment with the best finished product quality are found, themanufacturing parameters are not the optimal processing parameters ofthis type of production line equipment in practical application.

With the increasing improvement of intelligent manufacturing technology,how to use intelligent manufacturing technology to solve the abovetechnical problems is what is urgently needed to achieve.

SUMMARY

The technical problem to be solved by the present disclosure is toprovide industrial Internet of Things with a dual independent platform.The IIoT continuously executes different manufacturing parametersthrough the main production line device, so as to obtain the optimalmanufacturing parameters, and directly updates all sub-production linedevices through the IoT structure, so as to realize the intelligentoptimization and intelligent coverage of manufacturing parameters andimprove the finished product quality of all production line devices. Atthe same time, it simplifies the process of parameter update and reducesthe time and cost of data update.

The present disclosure is realized through the following technicalscheme: an industrial IoT with a dual independent platform, including auser platform, a service platform, a management platform, a sensornetwork platform and an object platform which are interacted insequence.

The user platform is configured as a terminal device, and interacts withusers.

The service platform is configured as the first server, receivesinstructions from the user platform and transmits to the managementplatform, and extracts information required to process the user platformfrom the management platform and transmits to the user platform.

The management platform is configured to the second server, controls theoperation of the object platform, and receives the feedback data of theobject platform.

The sensor network platform is configured as a communication network anda gateway for interaction between the object platform and the managementplatform.

The object platform is configured as production line devices and datacollectors for perform manufacturing.

The service platform adopts centralized layout, and the managementplatform and sensor network platform adopt independent layout. Thecentralized layout means that the service platform uniformly receivesdata, processes the data and sends the data. The independent layoutmeans that the management platform or the sensor network platform adoptsdifferent platforms for storage, processing and/or transmission of datafrom different object platforms.

The object platform includes a main production line device and at leastone sub-production line device of the same type as the main productionline device. The main production line device and several sub-productionline devices are equipped with data collectors.

When the main production line equipment performs manufacturing accordingto different configuration files at different execution times, the datacollector of the main production line equipment collects the finishedproduct parameters of the main production line device under the controlof different configuration files in a unit time, and transmits thefinished product parameters to the main platform of the sensor networkplatform. The different configuration files include a plurality ofmanufacturing parameters of the same type but with different values.

The main platform of the sensor network platform converts the finishedproduct parameters into data files that can be recognized by themanagement platform, and sends the data files to the main platform ofthe management platform;

The main platform of the management platform integrates the data fileswith the corresponding manufacturing parameters, selects the optimalfinished product parameter according to the calculation rule, andpackages and sends the manufacturing parameters corresponding to theoptimal finished product parameter to the service platform as the finalmanufacturing parameters;

The service platform receives and stores the final manufacturingparameter, when the user platform sends an instruction of executing thecovering manufacturing parameters, the service platform sends the finalmanufacturing parameters to at least one sub-platform of the managementplatform,

Each of at least one sub-platform of the management platform stores andprocesses the final manufacturing parameters and sends them to thesub-platform of the corresponding sensor network platform;

Each sub-platform of the sensor network platform receives the finalmanufacturing parameters, converts them into configuration files andsends them to each corresponding sub-production line device;

Each sub-production line device performs manufacturing according to thereceived configuration files.

Based on the above IoT technical scheme, the different configurationfiles of the main production line device are obtained in the followingways:

When the service platform receives the instruction from the userplatform to modify the manufacturing parameters, the service platformsends a modification instruction to the main platform of the managementplatform.

The main platform of the management platform receives the modificationinstruction and retrieves a pre-stored parameter data packet, andtransmits the parameter data packet to the corresponding main platformof the sensing network platform. The parameter data packet comprises atleast two manufacturing parameters of the same type with differentvalues and different execution times corresponding to differentmanufacturing parameters;

The main platform of the sensor network platform receives the parameterdata packet, sorts the at least two manufacturing parameters of theparameter data packet according to the order of the execution times toform at least two configuration files; and according to differentexecution times, sends the configuration files at the corresponding tothe execution times to the main production line device.

Based on the above IoT technical scheme, the parameter data packet is inthe form of one or more groups.

When the parameter data packet is in the form of multiple groups, themodification instruction at least includes an extraction time orextraction time interval of each group of parameter data packets.

The main platform of the management platform successively extracts theeach corresponding group of parameter data packets of according to theextraction time or extraction time interval.

Based on the above IoT technical scheme, when the covering manufacturingparameter instruction includes the execution time, the sub-platform ofthe management platform writes the execution time into the correspondingfinal manufacturing parameters.

After receiving the final manufacturing parameters, the sub-platform ofthe sensor network platform extracts the execution time, converts thefinal manufacturing parameters into configuration files, and sends theconfiguration files to the corresponding sub-production line deviceaccording to the execution time.

Based on the above IoT technology solutions, that the main platform ofthe management platform integrates the data files with the correspondingmanufacturing parameters, and selects the optimal finished productparameter according to a calculation rule, and packages and sends themanufacturing parameters corresponding to the optimal finished parameteras the final manufacturing parameter specifically includes:

sorting at least two finished product parameters in each of the datafiles according to an acquisition time or acquisition sequence of thedata collector to form a finished product parameter data groups whichare sequentially numbered, and sorting each manufacturing parameteraccording to the execution time or sequence to form manufacturingparameter data groups which are sequentially numbered.

associating the finished product parameter data groups with themanufacturing parameter data groups having the same number or sequenceto form several associated data packets;

selecting the optimal finished product parameter in the finished productparameter data groups according to the calculation rule by the mainplatform of the management platform; and

packaging and sending an associated data packet corresponding to theoptimal finished product parameter to the service platform.

Based on the above IoT technical scheme, the selecting the optimalfinished product parameter in the finished product parameter data groupsaccording to the calculation rule by the main platform of the managementplatform specifically includes:

presetting an ideal finished product parameter, subtracting all finishedproduct parameters in the finished product parameter data groups fromthe ideal finished product parameter in turn, respectively, obtaining afinished product parameter with the smallest difference from the idealfinished product parameter as the optimal finished product parameter incurrent different configuration files, and packaging and sending theassociated data packet corresponding to the optimal finished productparameter to the service platform; and

when the main production line device re-executes manufacturing accordingto another group of different configuration files, by the main platformof the management platform, obtaining an optimal finished productparameter for the another group of different configuration files in thesame subtraction operation modesubtracting the optimal finished productparameter for the another group of different configuration files and theoptimal finished product parameter for the previous group ofconfiguration files with the ideal finished product parameter, obtaininga finished product parameter with the smallest difference from the idealfinished product parameter as an optimal finished product parameter inthe current two groups of different configuration files, and packagingand sending an associated data package corresponding to the optimalfinished product parameter to the service platform.

The present disclosure also provides a control method of an IIoT with adual independent platform based on the above IIoT with the dualindependent platform. The IIoT with the dual independent platformincludes user platform, service platform, management platform, sensornetwork platform and object platform that interact in turn.

The service platform adopts centralized layout, and the managementplatform and sensor network platform adopt independent layout. Thecentralized layout means that the service platform uniformly receivesdata, processes data and sends data. The independent layout refers tothat the management platform or sensor network platform adopts differentplatforms for storage, processing and/or transmission of data fromdifferent object platforms.

The control method includes the following steps.

The object platform includes a main production line device and at leastone sub-production line devices of the same type as the main productionline device. The main production line device and several sub-productionline devices are equipped with data collectors.

When the main production line device performs manufacturing according tothe different configuration files at different execution times, the datacollector of the main production line device collects the finishedproduct parameters of the main production line device under the controlof different configuration files in unit time, and transmits thefinished product parameters to the main platform of the sensor networkplatform. The different configuration files include at least twomanufacturing parameters of the same type and different values.

The main platform of the sensor network platform converts the finishedproduct parameters into data files that can be recognized by themanagement platform, and sends the data files to the main platform ofthe management platform.

The main platform of the management platform integrates the data fileswith the corresponding manufacturing parameters, selects an optimalfinished product parameter according to a calculation rule, and packagesand sends the manufacturing parameters corresponding to the optimalfinished product parameter to the service platform as the finalmanufacturing parameters;

The service platform receives the final manufacturing parameters andstores them. When the user platform sends an instruction to execute theoverride manufacturing parameters, the service platform sends the finalmanufacturing parameters to at least one sub-platform of the managementplatform,

Each sub-platform of at least one sub platform of the managementplatform stores and processes the final manufacturing parameters andsends them to the sub platform of the corresponding sensor networkplatform;

Each sub-platform of the sensor network platform receives the finalmanufacturing parameters, converts them into configuration files andsends them to the corresponding sub-production line device;

Each sub-production line device performs manufacturing according to thereceived configuration files.

Based on the above control methods, the different configuration files ofthe main production line device are acquired by the following mannerthat:

when the service platform receives the instruction from the userplatform to change the manufacturing parameters, the service platformsends the modification instruction to the main platform of themanagement platform;

the main platform of the management platform receives the modificationinstruction, retrieves the pre-stored parameter data packets, andtransmits the parameter data packets to the main platform of thecorresponding sensor network platform; the parameter data packetcomprising at least two manufacturing parameters of the same type withdifferent values and different execution times corresponding todifferent manufacturing parameters;

the main platform of the sensor network platform receives the parameterdata packet, sorts the at least two manufacturing parameters of theparameter data packet according to the order of the execution times toform at least two configuration files, and according to the differentexecution times, sends the configuration files at the correspondingexecution times to the main production line device.

Compared with the prior art, the beneficial effects of some embodimentsof the application are as follows: the industrial Internet of thingswith the dual independent platform and its control method of someembodiments of the disclosure build the Internet of things based on thefive platform structure, in which the sensor network platform and themanagement platform are arranged independently, and each correspondingplatform includes at least one independent sub-platform. Thus,independent sensor network platform and management platform can beadopted for each production line device to form independent dataprocessing and transmission channels, reduce the data processing andtransmission capacity of each platform, and reduce the computingpressure of a single platform of the entire Internet of things. Allsensor network platforms and management platforms can be regulated andcontrolled through a centrally arranged service platform, this enablesthe service platform to better control the Internet of things.

When some of the embodiments of the present disclosure are used,combined with the IIoT with dual independent platform and its controlmethods, through the established main production line device, the mainplatform of the sensor network platform and the main platform of themanagement platform as the optimization system of manufacturingparameters, and through the main production line device to executedifferent manufacturing parameters to obtain the corresponding finishedproduct parameters, select the corresponding manufacturing parameterswith the optimal finished product parameters as the manufacturingparameters of other sub-production line devices for coverage andreplacement, so that all production line device can use the optimalmanufacturing parameters for intelligent manufacturing throughcontinuous updating and coverage, which can not only simplify theprocess and time of parameter screening and coverage, but also not toomuch affect the manufacturing operation of sub-production line device.The quality of finished products of all production line devices isimproved, and the quality of all production line devices and finishedproducts can be monitored at the same time, so as to facilitate theintelligent control of all production line device.

BRIEF DESCRIPTION OF THE DRAWINGS

The attached figures described here are used to provide furtherunderstanding of the embodiments of the present disclosure, whichconstitutes part of the present disclosure, which does not constitute alimitation of the embodiments of the present disclosure. In thedrawings:

FIG. 1 is schematic diagram illustrating a structural framework of IIoTwith a dual independent platform;

FIG. 2 is a flowchart for a control method of the IIoT with the dualindependent platform;

FIG. 3 is an exemplary flowchart of a process for determining targetmanufacturing parameters according to some embodiments of the presentdisclosure;

FIG. 4 is an exemplary flowchart of a process for determiningpreliminary target manufacturing parameters according to someembodiments of the present disclosure;

FIG. 5 is an exemplary flowchart of a process for determiningpreliminary target manufacturing parameters according to someembodiments of the present disclosure; and

FIG. 6 is an exemplary block diagram of a prediction model shownaccording to some embodiments of the present disclosure.

DETAILED DESCRIPTION

In order to make the purpose, technical scheme and advantages of thepresent disclosure clearer, the present disclosure is further describedin detail below in combination with the embodiments and drawings. Theschematic embodiment and description of the present disclosure are onlyused to explain the present disclosure and are not used as a limitationof the present disclosure. As used herein, the singular forms “a,” “an,”and “the” may be intended to include the plural forms as well, unlessthe context clearly indicates otherwise; and the plural forms may beintended to include the singular forms as well, unless the contextclearly indicates otherwise.

The first embodiment of the present disclosure aims to provide anindustrial IoT (IIoT) with dual independent platform. The IIoT with adual independent platform uses the five platforms IoT technology, e.g.,a sensor network platform and a management platform that form the dualindependent platform, and a centralized service platform, which can notonly realize the classified transmission and processing of data, butalso realize the overall control of data. The IIoT with the dualindependent platform can be widely used in intelligent production linesor intelligent assembly lines in various manufacturing industries, suchas medicine, food, mechanical device, electronic device and so on. TheIIoT with the dual independent platform has many characteristics, suchas clear classification for data transmission, low operation cost ofeach platform and easy data control.

As shown in FIG. 1 , the IIoT with the dual independent platformincludes a user platform, a service platform, a management platform, asensor network platform and an object platform.

The service platform adopts centralized layout, and the managementplatform and sensor network platform adopt independent layout. Thecentralized layout refers to the uniformly receiving data, processingdata, and sending data. The independent layout refers to the datastorage, data processing and/or data transmission of different platformson the management platform or sensor network platform.

The object platform includes a main production line device and at leastone sub-production line device of the same type as the main productionline device. The main production line device and the at least onesub-production line device are equipped with data collectors.

When the main production line device performs manufacturing according todifferent configuration files at different execution times, the datacollector of the main production line device collects the finishedproduct parameters of the main production line device under the controlof different configuration files in unit time, and transmits thefinished product parameters to the main platform of the sensor networkplatform. The different configuration files include at least twomanufacturing parameters of the same type but having different values.

The main platform of the sensor network platform converts the finishedproduct parameters into data files that can be recognized by themanagement platform, and sends the data files to the main platform ofthe management platform.

The main platform of the management platform integrates the data fileswith the corresponding manufacturing parameters, selects the optimalfinished product parameters according to the calculation rules, andpackages and sends manufacturing parameters corresponding to the optimalfinished product parameters as final manufacturing parameters to theservice platform.

The service platform receives and stores the final manufacturingparameters, when the user platform sends an instruction of executing thecovering manufacturing parameters, the service platform sends the finalmanufacturing parameters to a plurality of sub-platforms of themanagement platform.

Each of the sub-platforms of the management platform stores andprocesses the final manufacturing parameters and then sends them to acorresponding sub-platform of the sensor network platform.

Each of the sub-platforms of the sensor network platform receives thefinal manufacturing parameters, converts them into configuration filesand sends them to corresponding sub-production line devices.

Each of the sub-production line devices is manufactured according to thereceived configuration files.

As an existing IoT architecture, the user platform is configured asterminal devices and interacts with users. The service platform isconfigured as the first server, receives instructions of the userplatform and transmits them to the management platform, extractsinformation required to process the user platform from the managementplatform, and transmits the information to the user platform. Themanagement platform is configured as the second server, controls theoperation of the object platform, and receives the feedback data of theobject platform. The sensor network platform is configured as acommunication network and a gateway for the interaction between theobject platform and the management platform. The object platform isconfigured as a production line device and a data collector thatperforms manufacturing.

In the prior art, when there are a large number of production linedevices of the same type, the production line devices of the same typeoften perform manufacturing operations according to their presetmanufacturing parameters. In practical application, when each productionline device performs manufacturing under the control of manufacturingparameters, the finished product quality of each production line deviceis different due to different manufacturing parameters, causing thatsome production line devices of the same type have high quality offinished products and some has low quality of finished products, whichis not conducive to high-quality batch manufacturing requirements. Inorder to further improve the quality of the finished products for theproduction line dives with low quality of the finished products, it isnecessary to replace the parameters of multiple production line devices,respectively, which is not only time-consuming and laborious, but alsothe parameter replacement of multiple production line devices cannot becarried out at the same time, making some production line devices waitfor a long time to update. When updating the data, the new manufacturingparameters used to cover the original manufacturing parameters can onlybe calculated manually or extracted from the manufacturing parameterswith the best quality of a finished product, which is not the optimalmanufacturing parameters for the production line devices in the actualmanufacturing. Thus, the maximization and comprehensive improvement ofthe finished product quality cannot be achieved even if it takesmanpower, material resources and time to complete the parameterreplacement.

The IIoT with a dual independent platform of the present disclosure isconstructed based on a five platform structure, in which the sensornetwork platform and management platform are arranged independently,each of which includes a plurality of independent sub-platforms, so thatthe independent sensor network platform and management platform can beadopted for each production line device to form independent dataprocessing channel and transmission channel, so as to reduce the dataprocessing capacity and transmission capacity of each platform, reducethe computing pressure of a single platform of the whole IoT, anduniformly regulate all sensor network platforms and management platformsthrough the centrally arranged service platform, so that the serviceplatform can better manage and control the IoT.

As a result, the IoT with the dual independent platform of the presentdisclosure is used as a preferred system for manufacturing parametersthrough the main production line device, the main platform of the sensornetwork platform, and the main platform of the management platform. Themain production line devices execute different manufacturing parametersto obtain the corresponding finished product parameters, and select thecorresponding manufacturing parameters with the optimal finished productparameters as the manufacturing parameters of other sub-production linedevices for uniform coverage and replacement at the same time or atdifferent times. Thus, all production line devices can use the optimalmanufacturing parameters for intelligent manufacturing throughcontinuous updating and coverage. Excellent manufacturing parameters forintelligent manufacturing can not only simplify the process and time ofparameter screening and coverage, but also may not affect themanufacturing operation of the sub-production line device too much,maximize the quality of all production line devices, and at the sametime, the quality of all production line devices and its finishedproducts can be monitored, which facilitates the intelligent managementand control of all production line devices.

It should be noted that the user platform in this embodiment can bedesktop computer, tablet computer, notebook computer, mobile phone orother electronic devices that can realize data processing and datacommunication, which is not limited thereto. In a specific application,the first server and the second server can use a single server, or theserver cluster can also be used here. It should be understood that thedata processing process mentioned in the embodiments can be processed bythe processor of the server, and the data stored in the server can bestored on the storage device of the server, such as hard disk and othermemory. In specific applications, the sensor network platform can adoptmultiple groups of gateway servers or multiple groups of intelligentrouters, which are not limited here. It should be understood that thedata processing process mentioned in the embodiments of the presentapplication can be processed by the processor of the gateway server, andthe data stored in the gateway server can be stored on the storagedevice of the gateway server, such as hard disk, SSD and other memories.

It is further explained that in this industrial IoT with the dualindependent platform (also referred to as dual independent platformindustrial IoT), the sensor network platform and management platformadopt multiple (the same number) sub-platforms to form an independentlayout, while the multiple sub-platforms of the two platforms form aone-to-one corresponding parent-child relationship. In the actualapplication, each production line device corresponds to a sub-platformof the sensor network platform, so that the separate data processing,transmission and storage of different production line devices can berealized. It not only reduces the overall data processing, transmissionand storage capacity of the sensor network platform and managementplatform, but also carries out the data of different production linedevices separately, which can also avoid data errors, clear datasources, and ensure the independent safety control of production linedevices. The centralized layout of the service platform can ensure theunified coordination and management of all production line devices.

In some embodiments, the production line device is the various types ofproduction line device relied on the product manufacturing in theproduction line. Taking the mechanical product as an example, theproduction line device can be a variety of smart machine processingdevices such as smart lathes, smart milling machines, and smart plans.It can be a variety of modified device such as intelligent ignitiondevice, intelligent quenching device, smart coating device, etc.Correspondingly, its manufacturing parameters are parameters set by theproduction line device to achieve manufacturing, such as the car kniferoute parameters of the lathe, the recovery temperature of the recoverydevice, and the coating amount of the coating device. The finishedproduct parameter is the parameter value of the finished product made bythe production line device according to the manufacturing parameterunder the influence of the manufacturing parameter, such as, the sizeparameters of the finished product formed by the lathe under the controlof the turning tool path parameters, the toughness value of the finishedproduct formed by the tempering equipment under the control of thetempering temperature, and the thickness of the finished product formedby the coating equipment under the control of the coating amount, etc.Correspondingly, the data collector are various types of dataacquisition device that collects corresponding finished parameters, suchas obtaining the size of the finished product, the tough tester with thetoughness of the finished toughness, and the coating thicknessmeasurement instrument with the thickness of the finished coating. Itshould be noted that for different manufacturing parameters, there willbe different finished product parameters and data collectors. Therefore,in actual application, the manufacturing parameters can be determinedfirst, and then the corresponding finished parameter type and datacollector type can be determined.

In the prior art, when it is necessary to update or optimize theparameters of the production line device, better manufacturingparameters are generally obtained through screening or calculation. Boththe calculation amount and screening amount are extremely complex andtime-consuming, and the obtained manufacturing parameters are notnecessarily the optimal manufacturing parameters in the practicalapplication of the production line device, which makes it difficult toachieve how to quickly obtain the optimal manufacturing parameters inthe actual production.

Based on this, in the present disclosure, by setting the main productionline device, the main platform of the sensor network platform and themain platform of the management platform, as the screening andoptimization system of manufacturing parameters, can execute differentmanufacturing parameters to obtain different finished productparameters, and obtain the optimal manufacturing parameter among severalmanufacturing parameters through screening, so as to realize theintelligent optimization and intelligent screening of manufacturingparameters. Specifically:

When the service platform receives the instruction from the userplatform to change the manufacturing parameters, the service platformsends the modification instruction to the main platform of themanagement platform.

The main platform of the management platform receives the modificationinstruction, retrieves the pre-stored parameter data packet, andtransmits the parameter data packet to the corresponding main platformof the sensor network platform. The parameter data packet includes aplurality of manufacturing parameters of the same type but withdifferent values and different execution times corresponding todifferent manufacturing parameters.

The main platform of the sensor network platform receives the parameterdata packet, sorts a plurality of manufacturing parameters in theparameter data packet according to the order of execution times, andforms a plurality of configuration files. The main platform of thesensor network platform sends the configuration files corresponding tothe execution times to the main production line device according todifferent execution times.

It should be noted that the pre-stored parameter data package may beseveral manufacturing parameters obtained through screening orcalculation, or several manufacturing parameters set and selected by theproducer based on the ideal state or expected state according to theactual production. Further, the manufacturing parameters can be a fixedvalue. Of course, the manufacturing parameters of some devices cannot beaccurately fixed as a certain value, mostly in a small range. Forexample, the manufacturing parameters of tempering device are mostly inthe temperature range, such as 200-220° C. Therefore, in someembodiments, the manufacturing parameters can also be the interval valueof a certain range.

In the prior art, when the parameter data package involves multiplemanufacturing parameters, the main production line device may take along time to complete, which will lead to the failure of othersub-production line devices to quickly update the manufacturingparameters, and the optimal manufacturing parameters may be obtainedonly after the main platform of the management platform processes allthe finished product parameters, costing a long cycle. After obtainingsome preferred manufacturing parameters, it is also unable to update theparameters of the sub-production line device in time and maximize thefinished product quality of the sub-production line device.

Based on this, the IIoT with a dual independent platform of the presentdisclosure further optimizes the parameter data packet, specifically:

There are one or more groups of parameter data packets.

When the parameter data packet is multiple groups, the modificationinstruction includes at least the extraction time or extraction timeinterval of each group of parameter data packets.

The main platform of the management platform successively extracts thecorresponding groups of the parameter data packets according to theextraction time or extraction time interval.

By dividing relatively more manufacturing parameters into multiplegroups, when each group of configuration files is completed, theobtained optimal manufacturing parameter may be used for parameterupdate first, and the finished product quality of sub-production linedevices may be improved without a long cycle.

In some embodiments, in order to ensure the interval time and dataprocessing time of different manufacturing parameters, when differentmanufacturing parameters are required to participate in manufacturing atdifferent execution times, different manufacturing parameters can becontrolled to execute at different times by adding the execution time tothe manufacturing parameters, specifically in the following ways:

When an instruction of covering the manufacturing parameters containsthe execution time, the sub-platform of the management platform maywrite the execution time into the corresponding final manufacturingparameters.

After receiving the final manufacturing parameters, the sub-platform ofthe sensor network platform extracts the execution time, converts thefinal manufacturing parameters into configuration files, and sends theconfiguration files to the corresponding sub-production line deviceaccording to the execution time.

In some embodiments, the main platform of the management platformintegrates the data files with the corresponding manufacturingparameters, selects the optimal finished product parameter (or optimalfinished product parameters) according to the calculation rule, andpackages and sends the manufacturing parameters corresponding to theoptimal finished product parameter to the service platform as the finalmanufacturing parameters. Specifically:

Multiple finished product parameters in the data file are sortedaccording to the acquisition time or acquisition sequence of the datacollector to form sequentially numbered finished product parameter datagroups, and each manufacturing parameter are sorted according to theexecution time or sequence to form sequentially numbered manufacturingparameter data groups

A plurality of associated data packets are formed by associating thefinished product parameter data groups with the manufacturing parameterdata groups having the same number or sequence.

The main platform of the management platform selects the optimalfinished product parameter in the finished product parameter data groupsaccording to the calculation rule.

The main platform of the management platform packages and sends theassociated data package corresponding to the optimal finished productparameter to the service platform.

In some embodiments, the step that the main platform of the managementplatform selects the optimal finished product parameters in the finishedproduct parameter data group according to the calculation rulesspecifically includes the following steps.

The main platform of the management platform is preset with idealfinished product parameter, all finished product parameters in thefinished product parameter data group are subtracted from the idealfinished product parameter in turn, the finished product parameters withthe smallest difference from the ideal finished product parameter areobtained as the optimal finished product parameter under differentcurrent configuration files, and the corresponding associated datapackets are packaged and sent to the service platform.

When the main production line device performs manufacturing againaccording to another group of different configuration files, the mainplatform of the management platform obtains an optimal finished productparameter for the another group of different configuration files in thesame subtraction operation modesubtracts the optimal finished productparameter for the another group of different configuration files and theoptimal finished product parameter for the previous group ofconfiguration files with the ideal finished product parameter, obtains afinished product parameter with the smallest difference from the idealfinished product parameter as an optimal finished product parameter inthe current two groups of different configuration files, and packagesand sends an associated data package corresponding to the optimalfinished product parameter to the service platform.

It should be noted that the ideal finished product parameter is thefinished product parameters of the production line device in the idealstate or the best-finished product parameters expected by the producer,which should be better than (less likely equal) the finished productparameters in practical application.

As shown in FIG. 2 , the second embodiment of the present disclosureaims to provide a control method of IIoT with a dual independentplatform based on the above the IIoT with the dual independent platform.The IIoT with a dual independent platform includes user platform,service platform, management platform, sensor network platform, andobject platform which are successively interactive.

The service platform adopts centralized layout, and the managementplatform and sensor network platform adopt independent layout. Thecentralized layout means that the service platform uniformly receivesdata, uniformly processes data and uniformly sends data. The independentlayout means that the management platform or the sensor network platformadopts different platforms for storage, processing and/or transmissionof data from different object platforms.

The control method of IIoT with the dual independent platform includesthe following steps.

S1: The object platform includes a main production line device and atleast one sub-production line devices of the same type as the mainproduction line device, the main production line device and at least onesub-production line devices are equipped with data collectors.

S2: When the main production line device performs manufacturingaccording to different configuration files at different execution times,the data collector of the main production line device collects thefinished product parameters of the main production line device under thecontrol of different configuration files in unit time, and transmits thefinished product parameters to the main platform of the sensor networkplatform. The different configuration files include at least twomanufacturing parameters of the same type but having different values.

S3: The main platform of the sensor network platform converts thefinished product parameters into data files that can be recognized bythe management platform, and sends the data files to the main platformof the management platform;

S4: The main platform of the management platform integrates the datafiles with the corresponding manufacturing parameters, selects theoptimal finished product parameter according to the calculation rule,and packages and sends the manufacturing parameters corresponding to theoptimal finished product parameter to the service platform as the finalmanufacturing parameters;

S5: The service platform receives the final manufacturing parameters andstores them, and when the user platform sends an instruction of coveringthe manufacturing parameters, the service platform sends the finalmanufacturing parameters to at least one sub-platform of the managementplatform.

S6: Each sub-platform of at least one sub-platform of the managementplatform stores and processes the final manufacturing parameters andsends them to each corresponding sub-platform of the sensor networkplatform;

S7: Each sub-platform of the sensor network platform receives the finalmanufacturing parameters, converts them into configuration files andsends them to the each corresponding sub-production line device;

S8: Each sub-production line device performs manufacturing according tothe received configuration files.

Among them, the acquisition methods of different configuration files ofthe main production line device are as follows:

When the service platform receives an instruction issued by the userplatform to change the manufacturing parameter, the service platformsends a modification instruction to the main platform of the managementplatform.

The main platform of the management platform receives the modificationinstruction, retrieves the pre-stored parameter data packet, andtransmits the parameter data packet to the main platform of thecorresponding sensor network platform. The parameter data packetincludes at least two manufacturing parameters with different values ofthe same type and different execution times corresponding to differentmanufacturing parameters.

The main platform of the sensor network platform receives the parameterdata packet, sorts a plurality of manufacturing parameters in theparameter data packet according to the order of execution time, andforms a plurality of configuration files. The main platform of thesensor network platform sends the configuration files corresponding tothe execution time to the main production line device according todifferent execution times.

The following describes the IIoT with dual independent platform and itscontrol method by taking the automatic production line for mechanicalworkpiece processing as an example.

The production device on the automatic production line for machiningmechanical workpiece include CNC machine tools. CNC machine tool, alsoknown as computer numerical control machine tools, is an automaticmachine tool equipped with program control system. The control systemcan logically process the programs with control codes or other symbolicinstructions, decode them, express them in coded numbers, and input themto the NC device through the information carrier. After calculation andprocessing, the numerical control device can send various controlsignals to control the action of the machine tool, and automaticallyprocess the parts according to the shape and size required by thedrawing.

Generally speaking, CNC machine tools can include processing programcarrier, CNC device, servo and measurement feedback system and machinetool body.

The processing program carrier can store the parts processing program ina certain format and code on a program carrier, such as pore piercingpaper belts, box tapes, soft disks, etc. The part processing program mayinclude the relative motion path of the tool and workpiece on themachine tool, process parameters (such as feed rate, spindle speed,etc.) and auxiliary motion, etc. The processing program carrier caninput the above-stored part processing program (also known as NCinstruction) to the NC device through the input device of the NC machinetool.

Numerical control device is a position control system, which caninterpolate the ideal motion trajectory according to the input data. Thenumerical control device may be composed of three basic parts: inputunit, processing unit and output unit. The input unit can input theabove numerical control instructions to the processing unit (also knownas the numerical control unit), and there are different input unitsaccording to different program carriers. The input unit can includekeyboard input, disk input, CAD/CAM (computer aided design and computeraided manufacturing, referred to as CAD/CAM), system directcommunication input, direct NC input connected to the superior computer,etc., or any combination thereof. The processing unit can compile theabove numerical control commands (or instructions) into information thatcan be recognized by the computer, gradually store and process themaccording to the provisions of the control program, and send positionand speed instructions to a servo and measurement feedback systemthrough the output unit. The output unit may be associated with theservo and measurement feedback system. The output unit can receive theoutput pulse of the arithmetic unit according to the command of thecontroller, and transmit the output pulse to the servo and measurementfeedback system of each coordinate.

The servo and measurement feedback system can be used to realize thefeed servo control and spindle servo control of NC machine tools. Theservo and measuring feedback system can convert the command informationreceived from the numerical control device into the linear displacementor angular displacement of the executive components of the machine toolbody after power amplification and shaping.

The main body of the machine can include the mechanical part of variouscutting and processing (also known as execution parts, executivecomponents) automatically.

In addition, CNC machine tools can include auxiliary devices. Commonlyused auxiliary devices can include various auxiliary devices such aspneumatic, hydraulic device, crumb device, cooling device, lubricationdevice, and rotating workbench and CNC divisions, protection, andlighting.

Generally, the machining process of mechanical workpiece can include thefollowing processes: analyze drawings, determine machining parameters,cutting tools, etc., wherein machining parameters can include spindlespeed, tool feed speed, cutting speed, etc.; clamping the workpiece rawmaterials and cutting tools, and determining the workpiece zeroposition; and inputting processing parameters into processing program,processing and finishing product inspection.

In some embodiments, the object platform may include at least two CNCmachine tools. One of the CNC machine tools can be designated as themain CNC machine tool, and the remaining CNC machine tools can bedesignated as sub CNC machine tools. The main CNC machine tool can beused as screening and optimization device for manufacturing parameters.

The user platform can send instructions to the service platform tomodify the manufacturing parameters of the main NC machine tool. Themodified manufacturing parameters may include at least two sets ofprocessing parameters and their corresponding execution time. Each groupof machining parameters includes at least one of the spindle speed, toolfeed speed and cutting speed. Each group of processing parameterscorresponds to a group of finished products, and a group of finishedproducts includes at least one processed finished product. Afterreceiving, the service platform sends the modification instruction tothe main platform of the management platform. The main platform of themanagement platform can receive the modification instruction, retrievethe pre-stored parameter data packets, and transmit the parameter datapackets to the main platform of the corresponding sensor networkplatform. The main platform of the sensor network platform receivesparameter data packets, sorts at least two manufacturing parameters inthe parameter data packets according to the order of execution time, andforms at least two configuration files. The main platform of the sensornetwork platform sends the configuration files corresponding to theexecution time to the main CNC machine tool according to differentexecution times. The CNC device of the main CNC machine tool can receiveand process the above-mentioned configuration files, and ultimatelyenables the main body to automatically complete the processing procedureto obtain at least two groups of finished products.

When the main NC machine tool performs manufacturing according todifferent configuration files at different execution times, the datacollector of the main NC machine tool collects the finished productparameters of the main NC machine tool under the control of differentconfiguration files in a unit time, and transmits the finished productparameters to the main platform of the sensor network platform. The datacollector of the main CNC machine tool can be configured as a CCD(charge coupled device) visual detector. CCD visual detector can measurethe parameters of finished products and transmit the parameters to themain platform of the sensor network platform. Finished productparameters can include workpiece size, surface smoothness, etc.

The main platform of the sensor network platform converts the finishedproduct parameters into data files that can be recognized by themanagement platform, and sends the data files to the main platform ofthe management platform. The main platform of the management platformintegrates the data files with the corresponding manufacturingparameters, and selects the optimal finished product parameters inaccordance with the computing rule (or calculation rule), and sends themanufacturing parameters corresponding to the optimal finished parameteras the final manufacturing parameter to send to the service platform.The service platform receives and stores the final manufacturingparameters. When the user platform sends an instruction to covermanufacturing parameters, the service platform sends the finalmanufacturing parameters to at least one sub-platform of the managementplatform. Each sub-platform of the management platform corresponds to asub-platform of the sensor network platform and a sub-CNC machine tool.

Each sub-platform of the management platform stores and processes thefinal manufacturing parameters and sends them to the sub-platform of thecorresponding sensor network platform. Each sub-platform of the sensornetwork platform receives the final manufacturing parameters, convertsthem into configuration files and sends them to the correspondingsub-CNC machine tools. Each sub-CNC machine performs manufacturingaccording to the received configuration files.

FIG. 3 is an exemplary flowchart of a process for determining targetmanufacturing parameters according to some embodiments of the presentspecification. As shown in FIG. 3 , the process 300 includes thefollowing steps. In some embodiments, the process 300 may be executed bythe main platform of the management platform.

Step302, the main platform of the management platform can obtain thetest results of the finished products when the main production linedevice performs manufacturing (or processing) according to the differentconfiguration files at the different execution times. The test resultsinclude at least one test item, and the test results correspond to theparameters of the finished products. The configuration file can be afile that contains or can be converted into the processing parameters(also known as manufacturing parameters) of the production line, and theproduction line device can carry out production operations according tothe above processing parameters. Taking the production line of leafdrying in the tobacco processing as an example (the production lines inFIG. 3 and FIG. 4 are all use this production line example), theprocessing parameters can include the opening degree of the steam valve,return air temperature, moisture content of incoming material, cylinderwall temperature, incoming material flow, etc. Each configuration filecan correspond to a group of finished products, and a group of finishedproducts can correspond to at least one finished product or a batch offinished products. In some embodiments, the above differentconfiguration files may include at least two configuration files, andthe test results may include the test results of at least two groups offinished products. The test results of each group of finished productsmay include the measurement results of the finished parameters. In someembodiments, the finished product parameters may include a moisturecontent of the discharged material, an outlet temperature, etc.

The main production device of the object platform (such as the maintobacco leaf dryer) can produce according to the configuration files,and the data collector configured by the main production device is usedto obtain the test results. In some embodiments, the data collectorconfigured for the main tobacco leaf dryer may include a moisturecontent meter (for measuring the moisture content of the dischargedmaterial), a temperature sensor (for measuring the outlet temperature),or other instruments that can measure the test results.

Step 304, the main platform of the management platform can determine thepreliminary target manufacturing parameters based on the test results.The test results include two groups of test results of at least twogroups of finished products. The main platform of the managementplatform can select a group of optimal finished products from at leasttwo groups of products, and obtain the processing parameters (also knownas manufacturing parameters) corresponding to the group of optimalfinished products. The processing parameters corresponding to the groupof optimal finished products can be specified as the preliminary targetmanufacturing parameters. In some embodiments, the main platform of themanagement platform may select the group of optimal finished productsfrom at least two groups of products according to the standard productrequirements. The standard product requirements may include standardproducts required by users, standard products required by industrialstandards or national standards, standard products determined byhistorical qualified products, historical best products, etc. For thedetermination of preliminary target manufacturing parameters, see thedescription in FIG. 4 . In some embodiments, the main platform of themanagement platform may send preliminary target manufacturing parametersto the service platform.

Step 306, the service platform can adjust the preliminary targetmanufacturing parameters, determine the target manufacturing parameterscorresponding to the main production line device and the at least onesub-production line device respectively, and the target manufacturingparameters are included in the configuration files. The targetmanufacturing parameters are closer to manufacturing parameterscorresponding to the standard products than the preliminary targetmanufacturing parameters.

The target manufacturing parameters can be temporarily stored in theservice platform after being determined. When the user platform sendsthe covering manufacturing parameter instruction, the service platformcan send the target manufacturing parameters to the main platform and atleast one sub-platform of the management platform, and then transmitthem to the main production device and at least one sub-productiondevice of the object platform through the main platform and at least onesub-platform of the sensor network platform. The adjustment may be tomodify the preliminary target manufacturing parameters within the presetrange. In some embodiments, the service platform may adjust thepreliminary target manufacturing parameters according to the predictionmodel based on the standard product requirements. In some embodiments,the service platform may adjust the preliminary target manufacturingparameters according to the user's input. For the adjustments to thepreliminary target manufacturing parameters, see the description of FIG.5 for details.

By adjusting the preliminary target manufacturing parameterscorresponding to the optimal finished products, the target manufacturingparameters are closer to the manufacturing parameters corresponding tothe standard products than the preliminary target manufacturingparameters, and the finished products produced by the production linedevice are closer to the requirements of the standard products, so as toimprove the quality or qualification rate of the finished products.

FIG. 4 is an exemplary flowchart of a process for determiningpreliminary target manufacturing parameters according to someembodiments of the present disclosure. As shown in FIG. 4 , the process400 includes the following steps. In some embodiments, the process 400may be executed by the main platform of the management platform.

Step 402, the main platform of the management platform can obtain thetest results. As described in step 302, the test results may include thetest results of at least two groups of finished products. Each group offinished products can correspond to at least one finished product or abatch of finished products. Each group of finished products cancorrespond to a configuration file. The test results of each compositionmay include the measurement results of the finished parameters. In someembodiments, the finished product parameters may include a moisturecontent of the discharged material, an outlet temperature, etc. In someembodiments, the test results may be represented by at least one value.In some embodiments, the test results may be expressed as vectors. Insome embodiments, different weights may be assigned to differentfinished product parameters.

Step 404, the main platform of the management platform can obtain thestandard test results. The standard test results correspond to the testresults of the above finished products, which can be expressed in atleast one value or in a vector, and different weights can be assigned tothe parameters of the finished products. The standard test results canbe stored in the main platform of the management platform in advance.The exemplary standard test results are as follows: the standardmeasurement result of discharge moisture content is 13%, and thestandard measurement result of outlet temperature is 63° C.

In some embodiments, a standard test result may be an ideal test resultfor a finished product. In some embodiments, the standard test resultmay be a test result of a standard finished product. In someembodiments, considering the difference between actual production andtheoretical requirements, the main platform of the management platformcan determine the standard test results based on the actual situation.In some embodiments, the main platform of the management platform cancluster the test result vectors corresponding to the historical finishedproducts (products of the same type as the finished products) to obtaina plurality of clusters, then determine a cluster corresponding to acluster center closest to a vector corresponding to the ideal testresult, and determine the standard test results based on the cluster.

The cluster is determined by the following process. For each cluster inmultiple clusters, the main platform of the management platform canperform average or weighted average of the distances between the testresult vectors in the cluster and the ideal test result vector. Theweight of the weighted average is calculated by the distances betweenthe test result vectors and the ideal test result vector. For example,if the total distance from each test result vector to the ideal testresult vector in the cluster is L, the distance from the i^(th) testresult vector to the ideal test result vector is Li, and the totalnumber of test result vectors in the cluster is n, then the weight valueof each test result vector is (1−Li/L)/(n−1). The main platform of themanagement platform can select a cluster with the lowest average valueor weighted average value as the cluster used to determine the standardtest results.

The main platform of the management platform can determine the standardtest result based on the selected cluster. The main platform of themanagement platform can take the average or weighted average of all testresult vectors in the cluster, obtain an average or weighted averagevector as the standard test result vector, and obtain the standard testresult according to the standard test result vector. The weight of theweighted average is calculated by the distances between the test resultvectors and the ideal test result vector. The calculation method is thesame as that in the previous paragraph.

Step 406, the main platform of the management platform can determine thedifferences between the test results and the standard test result. Insome embodiments, the differences may be the distances between the testresult vectors and the standard test result vector. In some embodiments,the differences may be differences or weight differences between testresult values and a standard test result value. For test resultscontaining two or more measurement parameters, the main platform of themanagement platform can assign different weights to differentmeasurement parameter differences. The weights can be pre-set by theuser.

Step 408, the main platform of the management platform can determine thepreliminary target manufacturing parameters based on the differencesbetween the test results and the standard test result. In someembodiments, the main platform of the management platform can select thefinished product manufacturing parameters (also known as processingparameters) corresponding to the test results with the smallestdifference from the standard test result as the preliminary targetmanufacturing parameters. In some embodiments, the main platform of themanagement platform determines one or more test results according to apreset condition. The preset condition may be that a distance from atest result vector to the standard test result vector is less than apreset threshold. The main platform of the management platform can fusethe finished product manufacturing parameters corresponding to one ormore of the above test results to determine the preliminary targetmanufacturing parameters. In some embodiments, the main platform of themanagement platform can average the processing parameters correspondingto the test results whose differences meet the preset condition. In someembodiments, the main platform of the management platform can performthe weighted average of the processing parameters corresponding to thetest results whose differences meet the preset condition. The weight ofthe weighted average is calculated by the distances between the testresult vectors and the standard test result vector, and the calculationmethod is the same as that in step 404.

The preliminary target manufacturing parameters can be determinedthrough the differences between the test results of multiple groups offinished products and the standard test result, which can make thepreliminary target manufacturing parameters closer to the manufacturingparameter corresponding to the standard test result.

FIG. 5 is an exemplary flowchart of a process for determiningpreliminary target manufacturing parameters according to someembodiments of the present specification. As shown in FIG. 5 , theprocess 500 includes the following steps. In some embodiments, theprocess 500 may be executed by a service platform.

In step 502, the service platform may determine at least one set ofcandidate adjustment manners based on the preliminary targetmanufacturing parameters. The adjustment manner can include modifyingthe preliminary target manufacturing parameters within a certain range.In some embodiments, after determining the adjustment range, theadjustment intervals and corresponding adjustment sets within theadjustment range can be determined. The adjustment intervals may be thesame or different. The setting of adjustment intervals varies since thecategories of manufacturing parameters are different. The serviceplatform can adjust the preliminary target manufacturing parametersbased on the adjustment range and adjustment intervals to determine theat least one set of candidate adjustment manners.

In some embodiments, the service platform may acquire a presetadjustment range according to a preset instruction of a user.

In some embodiments, the service platform can determine at least one setof candidate adjustment manners according to the differences between thetest results corresponding to the preliminary target manufacturingparameters or the fusion test results and the standard test result. Thedifference may be the distances between the test result vectors orfusion test result vectors corresponding to the preliminary targetmanufacturing parameters and the standard test result vector, thenumerical value differences between the test results or fusion testresults corresponding to the preliminary target manufacturing parametersand the standard test result, or the weighted numerical differencesbetween the test results or fusion test results corresponding to thepreliminary target manufacturing parameters and the standard testresult.

In some embodiments, the service platform can set the functionalrelationship between the adjustment range and the differences describedabove, and the relationship between the adjustment range and thedifferences described above is a positive correlation. The serviceplatform can determine the adjustment range through the calculationresults of the above function relationship. The function may be a linearfunction, a quadratic function, a cubic function, or an exponentialfunction. For example, when the function is a linear function, thefunction relationship may be y=kl+a, where y represents the adjustmentrange, I represents the distance, and K and a are constants.

In some embodiments, the service platform may set a first threshold anda second threshold for the differences between the test results or thefusion test results and the standard test result. The first threshold isless than the second threshold. The threshold values can be preset inthe service platform by the user, and the user can adjust the values atany time. Different differences may cause different adjustment ranges,and the larger the difference, the larger the adjustment range. Forexample, when a difference is less than the first threshold, theadjustment range can be 10% of the preliminary target manufacturingparameters; when the difference is greater than the first threshold andless than the second threshold, the adjustment range can be 20% of thepreliminary target manufacturing parameters; when the difference isgreater than the second threshold, the adjustment range can be 30% ofthe preliminary target manufacturing parameters. Taking the temperingtemperature as an example, the preliminary target manufacturingparameter of tempering temperature is 118° C. and the adjustmentinterval is 0.5° C. When the difference is less than the firstthreshold, the adjustment range can be 11.8° C., and the adjustmentmanners can include 106.5° C., 107° C., 107.5° C. . . . 129° C., 129.5°C. When the difference is greater than the first threshold and less thanthe second threshold, the adjustment range can be 23.6° C., and theadjustment manners can include 94.5° C., 95° C., 95.5° C. . . . 141° C.,141.5° C. When the difference is greater than the second threshold, theadjustment range can be 35.4° C., and the adjustment method can include83° C., 83.5° C., 84° C. . . . 152.5° C., 153° C.

Step 504, the service platform may predict the test results of the atleast one set of candidate adjustment manners based on the predictionmodel to obtain the prediction test results. As shown in FIG. 6 , insome embodiments, the input of the prediction model is a candidateadjustment manner, and the output is a finished product prediction testresult. In some embodiments, the input of the prediction model alsoincludes the basic situation of the production line device. The basicinformation of the production line device can include device model,device maintenance record, device failure reason, device failure times,device service time, etc. The prediction model can be more accurate byinputting the basic information of the production line device.

In some embodiments, the prediction model may include a machine learningmodel. In some embodiments, the prediction model may include a neuralnetwork model. The prediction model can be obtained based on trainingsamples.

In some embodiments, the training samples may be training data andtraining tags. The training data includes at least one set of historicaladjustment manners of manufacturing parameters. The training tagincludes historical finished product test results corresponding to atleast one set of historical adjustment manners of manufacturingparameters. In some embodiments, the training data may also includehistorical basic information of a production line device used to producehistorical finished products.

In some embodiments, the above training samples may be input to theinitial prediction model for training to obtain the prediction model.

Step 506, the service platform may determine the target manufacturingparameters of the main production line device and the at least onesub-production line device based on the prediction test results. In someembodiments, for the target manufacturing parameters of the mainproduction line device and at least one sub-production line device, theservice platform can determine a set of target manufacturing parametersand apply them to all production line devices. In some embodiments, theservice platform may determine a set of target manufacturing parametersfor each of the main production line device and at least onesub-production line device, and apply them to the respective productionline devices. In some embodiments, for all production line devices or asingle production line device (when the input of the prediction modelincludes the basic situation (information) of the device), the serviceplatform can determine the differences between the prediction testresults and the standard test result, and take the preliminarymanufacturing parameter of the candidate adjustment mannerscorresponding to the prediction test result with the smallest differenceas the target manufacturing parameter. Through the prediction model, thetarget manufacturing parameters are filtered based on the predictionresults of at least one group of candidate adjustment manners, which canmake the filtered target manufacturing parameters closer to themanufacturing parameters corresponding to the standard test results.

It should be noted that the above description of relevant processes isonly for example and explanation, and does not limit the scope ofapplication of the present disclosure. It should be noted that the abovedescription of relevant processes is only for example and explanation,and does not limit the scope of application of the present disclosure.However, these amendments and changes are still within the scope of thepresent disclosure.

Those of ordinary skill in the art can realize that the units andalgorithm steps of each example described in conjunction with theembodiments disclosed herein can be implemented in electronic hardware,computer software, or a combination of the two. In order to clearlyillustrate the interchangeability of hardware and software, thecomponents and steps of each example have been generally described interms of functions in the foregoing description. These functions areexecuted in hardware or software, depending on the specific applicationand design constraints of the technical solution. Professional andtechnical personnel can use different methods to implement the describedfunctions on each specific application, but this implementation shouldnot be considered to exceed the scope of the disclosure.

In several embodiments provided in this disclosure, the device andmethods exposed should be implemented in other ways. For example, theembodiment of the device described above is only examples. For example,the division of the unit is only a logical function division. Inpractice, there can be another way to divide It can be integrated toanother system, or some features can be ignored or not executed.Additionally, the coupling or direct coupling or communicationconnection between the displayed or discussed may be indirectly coupledor communication between some interfaces, devices or units, or is alsoelectrically mechanically, mechanical or other forms.

The unit that is described as a separate part can be physical or not.The combination of electronic hardware, computer software, or thecombination of the two. In order to clearly explain theinterchangeability of the hardware and software, the composition andsteps of each example have been described in general in the abovedescription. These functions are executed in hardware or software,depending on the specific application and design constraints of thetechnical solution. Professional and technical personnel can usedifferent methods to implement the described functions on each specificapplication, but this implementation should not be considered to exceedthe scope of the disclosure.

In addition, each functional unit in each embodiment of the presentinvention can be integrated in one processing unit, or the physicalexistence of each unit alone, or two or more units integrated in oneunit. The above-mentioned integrated units can be implemented in theform of hardware or the form of software functional units.

The integrated unit can be stored in a computer readable storage mediumif implemented in the form of a software functional unit and is used asa stand-alone product. Based on this understanding, the technicalsolution of the present invention is essentially or contributed toexisting technology, or all or part of the technical solution can bereflected in the form of software products. The computer softwareproduct is stored in a storage medium in a storage medium Among them,there are several instructions to enable a computer device (can be apersonal computer, server, or grid device, etc.) to perform all or partof the methods described in each embodiment of the present invention.The aforementioned storage media include: USB flash disk, mobile harddisk, Read-Only Memory (ROM), Random Access Memory (RAM), magnetic discor optical disc and other media that can store program codes.

The specific embodiments described above further detail the purpose,technical scheme and beneficial effects of the present disclosure. Itshould be understood that the above are only the specific embodiments ofthe present disclosure and are not used to limit the protection scope ofthe present disclosure. Any modification, equivalent replacement,improvement, etc. made within the spirit and principles of the presentdisclosure should be included in the protection scope of the presentdisclosure.

We claim:
 1. A system of Industrial Internet of Things (IIoT) with adual independent platform, comprising a user platform, a serviceplatform, a management platform, a sensor network platform and an objectplatform which are interacted in sequence, wherein the service platformadopts centralized layout, and the management platform and the sensornetwork platform adopt independent layout; the centralized layout meansthat the service platform uniformly receives data, processes the dataand sends the data; the independent layout means that the managementplatform or the sensor network platform adopts different platforms forstorage, processing and/or transmission of data from different objectplatforms; the object platform includes a main production line deviceand at least one sub-production line device of the same type as the mainproduction line device, the main production line device and the at leastone sub-production line device are configured with data collectors,respectively; when the main production line device executesmanufacturing according to different configuration files at differentexecution times, the data collector of the main production line devicecollects finished product parameters of the main production line deviceunder control of the different configuration files in unit time andtransmits the finished product parameters to a main platform of thesensor network platform, the different configuration files including atleast two manufacturing parameters of the same type with differentvalues; the main platform of the sensor network platform converts thefinished product parameters into data files that can be recognized bythe management platform, and sends the data files to a main platform ofthe management platform; the main platform of the management platformintegrates the data files with the corresponding manufacturingparameters, selects an optimal finished product parameter according to acalculation rule, and packages and sends the manufacturing parameterscorresponding to the optimal finished product parameter to the serviceplatform as final manufacturing parameters; the service platformreceives the final manufacturing parameters and stores them, and whenthe user platform issues an instruction of executing coveringmanufacturing parameters, sends the final manufacturing parameters to atleast one sub-platform of the management platform; each of the at leastone sub-platform of the management platform stores and processes thefinal manufacturing parameters and sends them to each correspondingsub-platform of the sensor network platform; the each sub-platform ofthe sensor network platform receives the final manufacturing parameters,converts them into configuration files and sends them to eachcorresponding sub-production line device; and the each sub-productionline device performs manufacturing according to the receivedconfiguration files.
 2. The system of the IIoT with the dual independentplatform according to claim 1, wherein the different configuration filesof the main production line device are acquired by the following mannerthat: the service platform sends a modification instruction to the mainplatform of the management platform by the service platform when theservice platform receives the instruction from the user platform tomodify the manufacturing parameters; the main platform of the managementplatform receives the modification instruction, retrieves a pre-storedparameter data packet, and transmits the parameter data packet to thecorresponding main platform of the sensor network platform; theparameter data packet including at least two manufacturing parameters ofthe same type with different values and different execution timescorresponding to different manufacturing parameters; and the mainplatform of the sensor network platform receives the parameter datapacket, sorts the at least two manufacturing parameters of the parameterdata packet according to the order of the execution times to form atleast two configuration files, and according to the different executiontimes, sends the configuration files at the corresponding executiontimes to the main production line device.
 3. The system of the IIoT withthe dual independent platform according to claim 2, wherein theparameter data packet is in the form of one or more groups; themodification instruction at least includes an extraction time orextraction time interval of each group of parameter data packets whenthe parameter data packet is in the form of multiple groups; andaccording to the extraction time or extraction time interval, the mainplatform of the management platform extracts successively the eachcorresponding group of parameter data packets.
 4. The system of the IIoTwith the dual independent platform according to claim 1, wherein thesub-platform of the management platform writes an execution time intothe corresponding final manufacturing parameters when the instruction ofexecuting the covering manufacturing parameters includes the executiontime; and after receiving the final manufacturing parameters, thesub-platform of the sensor network platform extracts the execution time,converts the final manufacturing parameters into the configurationfiles, and sends the configuration files to the correspondingsub-production line device according to the execution time.
 5. Thesystem of the IIoT with the dual independent platform according to claim1, wherein that the main platform of the management platform integratesthe data files with the corresponding manufacturing parameters, selectsan optimal finished product parameter according to a calculation rule,and packages and sends manufacturing parameters corresponding to theoptimal finished product parameter to the service platform as finalmanufacturing parameters; calculation rule includes: sorting at leasttwo finished product parameters in each of the data files according toan acquisition time or acquisition sequence of the data collector toform finished product parameter data groups which are sequentiallynumbered, and sorting each manufacturing parameter according to theexecution time or sequence to form manufacturing parameter data groupswhich are sequentially numbered; associating the finished productparameter data groups with the manufacturing parameter data groupshaving the same number or sequence to form several associated datapackets; selecting the optimal finished product parameter in thefinished product parameter data groups according to the calculation ruleby the main platform of the management platform; and packaging andsending an associated data packet corresponding to the optimal finishedproduct parameter to the service platform.
 6. The system of the IIoTwith the dual independent platform according to claim 5, whereinselecting the optimal finished product parameter in the finished productparameter data groups according to the calculation rule by the mainplatform of the management platform includes: presetting an idealfinished product parameter, subtracting all finished product parametersin the finished product parameter data groups from the ideal finishedproduct parameter in turn, respectively, obtaining a finished productparameter with the smallest difference from the ideal finished productparameter as the optimal finished product parameter in current differentconfiguration files, and packaging and sending the associated datapacket corresponding to the optimal finished product parameter to theservice platform; and when the main production line device re-executesmanufacturing according to another group of different configurationfiles, by the main platform of the management platform, obtaining anoptimal finished product parameter for the another group of differentconfiguration files in the same subtraction operation mode subtractingthe optimal finished product parameter for the another group ofdifferent configuration files and the optimal finished product parameterfor the previous group of configuration files with the ideal finishedproduct parameter, obtaining a finished product parameter with thesmallest difference from the ideal finished product parameter as anoptimal finished product parameter in the current two groups ofdifferent configuration files, and packaging and sending an associateddata package corresponding to the optimal finished product parameter tothe service platform.
 7. The system of the IIoT with the dualindependent platform according to claim 1, wherein in order to integratethe data files with the corresponding manufacturing parameters andselect the optimal finished product parameter according to thecalculation rule, the main platform of the management platform furtherobtains test results of finished products manufactured by the mainproduction line device at the different execution times according to thedifferent configuration files, each of the test results including atleast one test item, and the test results corresponding to the finishedproduct parameters; and determines preliminary target manufacturingparameters based on the test results; and the service platform furtheradjusts preliminary target manufacturing parameters, and determinestarget manufacturing parameters corresponding to the main productionline device and the at least one sub-production line device,respectively, the target manufacturing parameters being included in theconfiguration files.
 8. The system of the IIoT with the dual independentplatform according to claim 7, wherein in order to determine thepreliminary target manufacturing parameters based on the test results,the main platform of the management platform further obtains standardtest results; and determines the preliminary target manufacturingparameters based on differences between the test results and thestandard test results.
 9. The system of the IIoT with the dualindependent platform according to claim 8, wherein in order to determinethe preliminary target manufacturing parameters based on the differencesbetween the test results and the standard test results, the mainplatform of the management platform further constructs test resultvectors based on the test results, and constructs standard test resultvectors based on the standard test results; and determines differencesbased on distances between the test result vectors and the standard testresult vectors.
 10. The system of the IIoT with the dual independentplatform according to claim 7, wherein in order to adjust thepreliminary target manufacturing parameters and determine the targetmanufacturing parameters corresponding to the main production linedevice and the at least one sub-production line device, respectively,the service platform further determines at least one set of candidateadjustment manners based on the preliminary target manufacturingparameters; predicts test results of the at least one set of candidateadjustment manners based on a prediction model to obtain prediction testresults; and determines the target manufacturing parameters of the mainproduction line device and the at least one sub-production line devicebased on the prediction test results.
 11. A control method of IndustrialInternet of Things (IIoT) with a dual independent platform, wherein theIIoT with the dual independent platform comprises a user platform, aservice platform, a management platform, a sensor network platform andan object platform which are interacted in sequence; the serviceplatform adopts centralized layout, and the management platform and thesensor network platform adopt independent layout; the centralized layoutmeans that the service platform uniformly receives data, processes thedata and sends the data; the independent layout means that themanagement platform or the sensor network platform adopts differentplatforms for storage, processing and/or transmission of data fromdifferent object platforms; the object platform includes a mainproduction line device and at least one sub-production line device ofthe same type as the main production line device, the main productionline device and the at least one sub-production line device areconfigured with data collectors, respectively; and the control methodcomprises: when the main production line device performs manufacturingaccording to different configuration files at different execution times,collecting, by a data collector of the main production line device,finished product parameters of the main production line device undercontrol of the different configuration files in unit time, andtransmitting the finished product parameters to a main platform of thesensor network platform, wherein the different configuration filesincludes at least two manufacturing parameters of the same type withdifferent values; converting, by the main platform of the sensor networkplatform, the finished product parameters into data files that can berecognized by the management platform, and sends the data files to amain platform of the management platform; integrating, by the mainplatform of the management platform, the data files with thecorresponding manufacturing parameters, selecting an optimal finishedproduct parameter according to a calculation rule, and packaging andsending manufacturing parameters corresponding to the optimal finishedproduct parameter to the service platform as final manufacturingparameters; receiving, by the service platform, the final manufacturingparameters and storing them, and when the user platform issues aninstruction of executing covering manufacturing parameters, sending thefinal manufacturing parameters to at least one sub-platform of themanagement platform; storing and processing, by each of the at least onesub-platform of the management platform, the final manufacturingparameters and sending them to each corresponding sub-platform of thesensor network platform; receiving, by the each sub-platform of thesensor network platform, the final manufacturing parameters, convertingthem into configuration files and sending them to each correspondingsub-production line device; and performing, by the each sub-productionline device, manufacturing according to the received configurationfiles.
 12. The control method according to claim 11, wherein thedifferent configuration files of the main production line device areacquired by the following manner that: the service platform sends amodification instruction to the main platform of the management platformby the service platform when the service platform receives theinstruction from the user platform to modify the manufacturingparameters; the main platform of the management platform receives themodification instruction, retrieves a pre-stored parameter data packet,and transmits the parameter data packet to the corresponding mainplatform of the sensor network platform; the parameter data packetincluding at least two manufacturing parameters of the same type withdifferent values and different execution times corresponding todifferent manufacturing parameters; and the main platform of the sensornetwork platform receives the parameter data packet, sorts the at leasttwo manufacturing parameters of the parameter data packet according tothe order of the execution times to form at least two configurationfiles, and according to the different execution times, sends theconfiguration files at the corresponding execution times to the mainproduction line device.
 13. The control method according to claim 12,wherein the parameter data packet is in the form of one or more groups;the modification instruction at least includes an extraction time orextraction time interval of each group of parameter data packets whenthe parameter data packet is in the form of multiple groups; andaccording to the extraction time or extraction time interval, the mainplatform of the management platform extracts successively the eachcorresponding group of parameter data packets.
 14. The control methodaccording to claim 11, wherein the control method further comprises:writing, by the sub-platform of the management platform, an executiontime into the corresponding final manufacturing parameters when theinstruction of executing the covering manufacturing parameters includesthe execution time; after receiving the final manufacturing parameters,extracting the execution time, converting the final manufacturingparameters into the configuration files, and sending the configurationfiles to the corresponding sub-production line device according to theexecution time by the sub-platform of the sensor network platform. 15.The control method according to the claim 11, wherein by the mainplatform of the management platform, integrating the data files with thecorresponding manufacturing parameters, selecting the optimal finishedproduct parameters according to the calculation rule, and packaging andsending the manufacturing parameters corresponding to the optimalfinished product parameter to the service platform as finalmanufacturing parameters comprises: sorting at least two finishedproduct parameters in each of the data files according to an acquisitiontime or acquisition sequence of the data collector to form finishedproduct parameter data groups which are sequentially numbered, andsorting each manufacturing parameter according to the execution time orsequence to form manufacturing parameter data groups which aresequentially numbered; associating the finished product parameter datagroups with the manufacturing parameter data groups having the samenumber or sequence to form several associated data packets; selectingthe optimal finished product parameter in the finished product parameterdata groups according to the calculation rule by the main platform ofthe management platform; and packaging and sending an associated datapackage corresponding to the optimal finished product parameter to theservice platform.
 16. The control method according to claim 15, whereinselecting the optimal finished product parameter in the finished productparameter data groups according to the calculation rule by the mainplatform of the management platform comprises: presetting an idealfinished product parameter, subtracting all finished product parametersin the finished product parameter data groups from the ideal finishedproduct parameter in turn, respectively, obtaining a finished productparameter with the smallest difference from the ideal finished productparameter as the optimal finished product parameter in current differentconfiguration files, and packaging and sending the associated datapackets corresponding to the optimal finished product parameter to theservice platform; and when the main production line device re-executesmanufacturing according to another group of different configurationfiles, by the main platform of the management platform, obtaining anoptimal finished product parameter for the another group of differentconfiguration files in the same subtraction operation mode subtractingthe optimal finished product parameter for the another group ofdifferent configuration files and the optimal finished product parameterfor the previous group of configuration files with the ideal finishedproduct parameter, obtaining a finished product parameter with thesmallest difference from the ideal finished product parameter as anoptimal finished product parameter in the current two groups ofdifferent configuration files, and packaging and sending an associateddata package corresponding to the optimal finished product parameter tothe service platform.
 17. The control method according to the claim 11,wherein by the main platform of the management platform, integrating thedata files with the corresponding manufacturing parameters, andselecting the optimal finished product parameter according to thecalculation rule comprises: obtaining test results of finished productsmanufactured by the main production line device at the differentexecution times according to the different configuration files, each ofthe test results including at least one test item, and the test resultscorresponding to the finished product parameters; determiningpreliminary target manufacturing parameters based on the test results;and the control method further comprises: adjusting preliminary targetmanufacturing parameters, and determining target manufacturingparameters corresponding to the main production line device and the atleast one sub-production line device, respectively, the targetmanufacturing parameters being included in the configuration files. 18.The control method according to claim 17, wherein the determining thepreliminary target manufacturing parameters based on the test resultscomprises: obtaining standard test results; and determining thepreliminary target manufacturing parameters based on differences betweenthe test results and the standard test results.
 19. The control methodaccording to the claim 18, wherein the determining the preliminarytarget manufacturing parameters based on the differences between thetest results and the standard test results comprises: constructing testresult vectors based on the test results, and constructing standard testresult vectors based on the standard test results; and determiningdifferences based on distances between the test result vectors and thestandard test result vectors.
 20. The control method according to claim17, wherein the adjusting the preliminary target manufacturingparameters and determining the target manufacturing parameterscorresponding to the main production line device and the at least onesub-production line device, respectively comprises: determining at leastone set of candidate adjustment manners based on the preliminary targetmanufacturing parameters; predicting test results of the at least oneset of candidate adjustment manners based on a prediction model toobtain prediction test results; and determining the target manufacturingparameters of the main production line device and the at least onesub-production line device based on the prediction test results.